INT8 Quantized Engine

Model assetModel ServingModelsarc:INT8InferenceEngine

A compiled model engine with 8-bit integer weights and activations whose quantization parameters come from calibration on representative inputs.

Responsibility. Serves the model at calibrated INT8 precision.

Also known as: INT8 calibrated engine, INT8 SmoothQuant engine

Variant of Optimized Inference Engine abstract

When to choose. Choose when 1-2% accuracy reduction is tolerable (e.g., conversational or human-reviewed moderation workloads) in exchange for roughly halved infrastructure cost; requires representative calibration.

Direct neighbourhood (hover for relationship types)

Relationships

deployed on structural

is configured by structural

Quantization Calibration Dataset Ch4.4

is evaluated by assurance

Evaluation Harness Ch7.2

is produced by lifecycle

alternative to variability

Design guidance

SHOULD calibrate on data statistically similar to production inputs; MAY maintain separate calibrated builds per workload domain.

Quantitative guidance

As stated by the sources; verify before use.

Well-calibrated INT8 typically within 1-2% of FP16 on MMLU (Ch4.4).
Moderation example: 2 GPUs, 324 req/s (162.4 req/s/GPU, 3.59x per-GPU), 95.9% accuracy (-0.9%), $12,000/month, $288,000 annual savings (Ch4.4).
GPT-2: 11ms/token, 2.2GB (68% reduction), 91 tok/s (3.8x), 98.5% of FP32 accuracy on held-out set, 8-12 concurrent users (Ch4.6).
INT8 SmoothQuant: 2x throughput, 50% memory saving, ~1-3% accuracy loss (Ref4.06).
INT8: 2x batch size + 20% kernel speedup = 2.4x throughput, 50% cost savings, typically <2% accuracy loss; 20-30% latency improvement (Ch7.2).
7B on A100: 160-180 tokens/s at batch 8 vs 100 for FP16 (60-80% gain), 40-50% lower per-token latency (Ch7.4).
MMLU 68.2% (FP16) vs 67.5% (INT8): <1 point degradation when well calibrated (Ch7.4).
Throughput +60% vs FP16, <1% accuracy loss, 75% memory reduction (Ref7.01).

Classification

Patterns: Post-training calibrationPer-domain calibrated builds
Risks mitigated: Excess GPU cost

Sources

Ch4.4: T. Nguyen, "Performance Profiling and Optimization," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 4.4. ISBN: 9798244538229.
Ch4.6: T. Nguyen, "TensorRT-LLM and NVIDIA Fleet Command," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 4.6. ISBN: 9798244538229.
Ch7.1B: T. Nguyen, "Nvidia NIM and Colang," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 7.1B. ISBN: 9798244538229.
Ch7.2: T. Nguyen, "Performance Optimization and Production Monitoring," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 7.2. ISBN: 9798244538229.
Ch7.4: T. Nguyen, "TensorRT-LLM Fundamentals and Quantization," in Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam, 1st ed. 2026, ch. 7.4. ISBN: 9798244538229.
Ref4.01: NVIDIA, "TensorRT-LLM," GitHub repository. Accessed: Sep. 27, 2026. [Online]. Available: https://github.com/NVIDIA/TensorRT-LLM
Ref7.01: NVIDIA, "Best practices," NVIDIA TensorRT Documentation. Accessed: Sep. 27, 2026. [Online]. Available: https://docs.nvidia.com/deeplearning/tensorrt/latest/performance/best-practices.html
Ref7.04: NVIDIA, "NVIDIA NIM," NVIDIA Docs. Accessed: Sep. 27, 2026. [Online]. Available: https://docs.nvidia.com/nim/
Ref7.15: "Advanced Agentic AI Optimization Techniques," unpublished reference note (15-Advanced-Agentic-Optimization.md), Mastering Agentic AI Systems: Guide for the NVIDIA NCP-AAI Exam supplementary materials, 2026. unpublished note